Microwave radiometry over Titan's seas and lakes
Bibliographic record
Abstract
In its passive, or radiometry, mode of operation, the Cassini Radar measures the microwave thermal emission from the surface at a wavelength of 2.2 cm. In doing so, it provides unique insight into surface properties of Saturn's largest moon Titan such as physical temperature, overall composition and structure (roughness, heterogeneity...). To date, almost the whole surface of Titan has been mapped by the Cassini Radiometer , whose calibration has been recently refined resulting in an unprecedented accuracy of about 1%. The measured brightness temperatures have also been referenced to the same epoch (i.e. 2005 based on CIRS observations of seasonal surface temperature variations) and to normal incidence. This allows the use of measurements performed at different epochs and with different observational geometries to compare the emissivities of different geological units on Titan. In particular, comparison of radiometry data acquired over Titan's seas and lakes at different places and times should provide clues to their composition and potential seasonal variations. In this paper, we will mainly focus on the radiometry data collected over the northern seas Ligeia Mare and Kraken Mare and the southern lake Ontario Lacus. These three features have been observed several times over the course of the Cassini mission, both in SAR-radiometry and altimetry-radiometry modes of operation. In all cases, assuming no evaporative cooling, radiometry data point to a dielectric constant of about 1.70×0.25, consistent with liquid hydrocarbons. Comparison of radiometry at sea with nearby onshore measurements may allow us to detect evaporative cooling. This will be investigated and further discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".